3D volume imaging is a diagnostic tool that offers significant advantages over earlier 2D radiographic imaging techniques for evaluating the condition of internal structures and organs. 3D imaging of a patient or other subject has been made possible by a number of advancements, including the development of high-speed imaging detectors, such as digital radiography (DR) detectors that enable multiple images to be taken in rapid succession.
Cone beam computed tomography (CBCT) or cone beam CT technology offers considerable promise as one type of diagnostic tool for providing 3D volume images. Cone beam CT systems capture volume data sets by using a high frame rate flat panel digital radiography (DR) detector and an x-ray source, typically affixed to a gantry that revolves about the object to be imaged, directing, from various points along its orbit around the subject, a divergent cone beam of x-rays toward the subject. The CBCT system captures projection images throughout the source-detector orbit, for example, with one 2D projection image at every degree increment of rotation. The projections are then reconstructed into a 3D volume image using various algorithmic techniques. Among the most common methods for reconstructing the 3D volume image are filtered back projection (FBP) approaches. An exemplary reconstruction approach is described, for example, in the paper by L. A. Feldkamp, L. C. Davis, and J. W. Kress, entitled “Practical cone-beam algorithm,” Journal of the Optical Society of America, vol 1, pp. 612-619, June, 1984.
Although 3D images of diagnostic quality can be generated using CBCT systems and technology, a number of technical challenges remain. Highly dense objects, such as metallic implants, prostheses and related appliances, surgical clips and staples, dental fillings, and the like can cause various image artifacts that can obscure useful information about the imaged features. This occurs because dense objects having a high atomic number attenuate X-rays in the diagnostic energy range much more strongly than do soft tissue or bone features. When dense structures are in the exposure path, fewer photons reach the imaging detector through these objects. For 3D imaging, the image artifacts that can be generated in reconstruction routines by metallic and other highly dense objects include dark and bright streaks that spread across the entire reconstructed image. Such artifacts can be due to physical effects such as high quantum noise, radiation scatter, beam hardening, and non-linear amplification in reconstruction algorithms. These artifacts, generically referred to as metallic artifacts or metal artifacts, can reduce image quality by masking soft tissue structures, not only in the immediate vicinity of the dense object, but also throughout the entire image. Without some type of compensation, this can falsify CT values and even make it difficult or impossible to use the reconstructed image effectively in assessing patient condition or properly planning radiation therapy or other treatments.
Various approaches have been tried for metal artifacts reduction (MAR), with varying success and some shortcomings. Three types of approaches include:
1. Interpolation-based FBP reconstruction approach. This approach operates in the projection domain, where the metal feature or shadow is identified and obscured values are interpolated using nonmetal-contaminated neighbors. For some types of imaging, with a single metal object within a relatively homogeneous volume, this method works acceptably. However, in more complex heterogeneous tissue, particularly where there are multiple metal objects in a heterogeneous volume, the interpolation-based algorithm can make unrealistic assumptions about the volume segment that lies in the shadow of the feature or object(s), leading to prominent errors in the reconstructed images. Theoretically, it is known in the 3D imaging arts that any interpolation-based repair scheme of the Radon space is based on a weak underlying model. Hence, it cannot be expected that the estimated projection data will suitably fit the projection data if measured without metal objects.
2. Iterative reconstruction approach. Generally improved over the performance of interpolation-based FBP described in approach (1), the iterative reconstruction approach is also more successful for severely distorted images. Iterative reconstruction uses some prior knowledge of the image physics, noise properties, and imaging geometry of the system. For this method, it is necessary to have information about the shape and location and, possibly, the attenuation coefficients of the metal objects in the reconstruction image domain. Typically, a constrained optimization approach is applied, which can be very sensitive to system configurations and to the quality of the projection data. These requirements are easily met for computer simulation or phantom imaging, and have been experimentally tested by researchers; however, iterative reconstruction may be impractical for clinical use, where volume geometries are considerably more complex than those used in simulation. Furthermore, iterative reconstruction methods are computationally much more intensive than FBP, making these methods less practical for clinical use in commercial CT scanning apparatus.
3. Quasi-iterative based FBP approach. The quasi-iterative based FBP approach performs clustering in the reconstruction domain after the initial 3D image reconstruction, without any metal correction or with metal correction introduced in approach (1) (above). The voxel elements of the reconstructed volume are classified into several tissues, such as soft tissue, bone, air, etc., with each voxel assigned a value corresponding to one of these tissue types. This method then forward projects the classified reconstruction volume onto each metal-affected detector element and subsequently generates a final reconstruction of the thus modified raw data to obtain the metal artifacts reduced volume. This method outperforms the interpolation-based FBP approach. The most prominent feature of this method is suppression of secondary artifacts caused by the interpolation scheme. However, one drawback of this method is that it fails whenever the interpolation based approach (1) fails. Moreover, quasi-iterative processing cannot handle the case where the object size exceeds the field of view, since additional artifacts caused by the forward projection are introduced in the corrected images.
An exemplary MAR approach is described, for example, by W. A. Kalender, R. Hebele, and J. Ebersberger, in an article entitled “Reduction of CT artifacts caused by metallic implants”, Radiology 164(2), 576-577 (1987).
It is recognized that metal artifacts reduction is a challenging task, particularly where implant geometries may be more complex. There is a need for methods of metal artifacts reduction that offer performance and computational efficiency.